I am working with loan data. I have a field for "months since last delinquency". If a borrower has not been delinquent on any of their accounts for the past 7 years, this field has missing value (
NA). As you can see, this is a "genuine" case of missing data.
In the dataset I got, the field is missing only for less than 4% of the data points (306 out of 8965), but dropping the rows will exclude the "good" borrowers and bias the dataset. Also, I believe this field is of value for prediction purposes, so I don't want to remove it.
I know tree-based models can handle missing values. In fact, I already have a model built with XGBoost and it has decent performance.
Now I want to build a simpler linear regression model (with regularization) for making the case that the XGBoost model is worth its complexity. This requires me to impute these missing values.
What is the value I can use for imputing the missing values? Setting it to 84 (number of months in 7 years) seems to make some sense, but that would mean the borrower was last delinquent 84 months ago, which is not true. I am also worried about imputing the value to something very large (like 999), since these points may then have high leverage.
Here is the summary of the data (in R code):
> nrow(loans)  8965 > summary(loans$MONTHS_SINCE_DEL) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 0.000 0.000 1.000 5.058 3.000 81.000 306
How does one deal with this problem in practice, when working with models that cannot handle missing values?